advanced data analytics

Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.

Download this whitepaper to see how advanced technologies such as big data, cloud computing, mobile devices, and enterprise access to in-memory platforms, predictive analytics, and planning software can help CFOs make better and more sophisticated use of data, influence decisions, and take practical, timely action.

In today’s world, advanced vision technologies is shaping the next era of Internet of Things. However, gathering streaming video data is insufficient. It needs to be timely and accessible in near-real time, analyzed, indexed, classified and searchable to inform strategy—while remaining cost-effective.
Smart cities and manufacturing are prime examples where complexities and opportunities have been enabled by vision, IoT and AI solutions through automatic meter reading (AMR), image classification and segmentation, automated optical inspection (AOI), defect classification, traffic management solution—just to name a few.
Together, ADLINK, Touch Cloud, and Intel provide a turnkey AI engine to assist in data analytics, detection, classification, and prediction for a wide range of use cases across a broad spectrum of sectors.
Learn more about how the Touch Cloud AI brings cost savings, operational efficiency and a more reliable, actionable intelligence at the edge with transformative insi

The headlines are ablaze with the latest stories of cyberattacks and data breaches. New malware and viruses are revealed nearly every day. The modern cyberthreat evolves on a daily basis, always seeming to stay one step ahead of our most capable defenses. Every time there is a cyberattack, government agencies gather massive amounts of data. To keep pace with the continuously evolving landscape of cyberthreats, agencies are increasingly turning toward applying advanced data analytics to look at attack data and try to gain a deeper understanding of the nature of the attacks. Applying modern data analytics can help derive some defensive value from the data gathered in the aftermath of an attack, and ideally avert or mitigate the damage from any future attacks.

Companies are finding more success with advanced analytics initiatives, allowing for continued innovation and experimentation. Read the report learn key findings from the survey carried out by The Economist Intelligence Unit, with Sponsorship by SAP.

A&BI platforms are evolving beyond data visualization and dashboards to encompass augmented and advanced analytics. Data and analytics leaders should enable a broader set of users with new expanded capabilities to increase the business impact of their investments.

Enterprise analytics is quickly evolving into a democratized capability where anyone can access and act on all available information, often in real-time employing advanced techniques. But the complex, dynamic and urgent nature of modern data analytics demands a new approach to data integration.
This paper proposes that DataOps, the application of DevOps practices to data analytics, is the best way to overcome these challenges to create an iterative build-operate process for data movement.
Read this white paper to:
Understand how modern data analytics create data integration challenges due to architectural complexity, operational blindness and data drift.
Learn how DevOps pillars of automation and monitoring can create higher developer productivity, operational efficiency and business confidence in data.
See specific examples of DataOps functionality being applied to data integration across modern architectures.

Enterprise analytics has quickly evolved from a centralized business intelligence function for historical reporting and dashboards to a democratized capability where anyone can access, analyze and act on all available information, often in real-time while employing advanced techniques. But the complex, dynamic and urgent nature of modern data analytics demands a new approach to data integration.

Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.

Large organizations can no longer rely on preventive security systems, point security tools, manual processes, and hardened configurations to protect them from targeted attacks and advanced malware.
Henceforth, security management must be based upon continuous monitoring and data analysis for up-to-the-minute situational awareness and rapid data-driven security decisions. This means that large organizations have entered the era of data security analytics.
Download here to learn more!

Health insurers have long been plagued by issues of fraud, waste, abuse, error and corruption. Taking an enterprise approach to payment integrity – one that combines advanced data management and sophisticated analytics – can help payers detect and prevent fraud; effect positive change in how providers, employees and patients behave; and substantially reduce health care costs. Payers can achieve better outcomes when software support for the core disciplines of payment integrity run on a single platform.

With the advanced analytics capabilities in Adobe Analytics and the testing and targeting capacity of Adobe Target, it’s easier than ever to realise the potential of data-driven marketing. From creating a complete view of each customer across touchpoints and along their journey, to using predictive analytics, advanced anomaly detection and machine learning to understand behaviours and needs, you can use data to plan, create and optimise the experiences that matter to you and your customers.

When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for
the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics, and operations. Even so, traditional, latent data practices are possible, too.
Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and
discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data. With the
right end-user tools, a data lake can enable the self-service data practices that both technical and business users need. These practices wring business value from big data, other new data sources, and burgeoning enterprise da

For data scientists and business analysts who prepare data for analytics, data management technology from SAS acts like a data filter – providing a single platform that lets them access, cleanse, transform and structure data for any analytical purpose. As it
removes the drudgery of routine data preparation, it reveals sparkling clean data and adds value along the way. And that can lead to higher productivity, better decisions and greater agility.
SAS adheres to five data management best practices that support advanced analytics
and deeper insights:
• Simplify access to traditional and emerging data.
• Strengthen the data scientist’s arsenal with advanced analytics techniques.
• Scrub data to build quality into existing processes.
• Shape data using flexible manipulation techniques.
• Share metadata across data management and analytics domains.

Today’s consumers expect immediate, personalized interactions. To meet these expectations, companies must differentiate their brands through timely, targeted and tailored customer experiences based on real-time data analytics.
This report, sponsored by SAS, Intel and Accenture and conducted by Harvard Business Review Analytic Services, looks at how businesses are using advanced customer data analytics, along with real-time analytics and real-time marketing, to enhance their customers’ experiences.
Learn why organizations that place a high value on real-time capabilities still struggle to achieve them, what companies can do to ensure success as they adopt and implement real-time analytics solutions, and what benefits successful companies are already seeing.

When designed well, a data lake is an effective data-driven design pattern for capturing a wide range of data types, both old and new, at large scale. By definition, a data lake is optimized for the quick ingestion of raw, detailed source data plus on-the-fly processing of such data for exploration, analytics and operations. Even so, traditional, latent data practices are possible, too.
Organizations are adopting the data lake design pattern (whether on Hadoop or a relational database) because lakes provision the kind of raw data that users need for data exploration and discovery-oriented forms of advanced analytics. A data lake can also be a consolidation point for both new and traditional data, thereby enabling analytics correlations across all data.
To help users prepare, this TDWI Best Practices Report defines data lake types, then discusses their emerging best practices, enabling technologies and real-world applications. The report’s survey quantifies user trends and readiness f

Many small and midsize retailers could benefit from using advanced analytics to understand their customers better and improve promotions but are daunted by the prospect. They are aware that advanced analytics can help retailers turn purchase data into retail excellence. However, they perceive that advanced analytics requires massive infrastructure changes, expensive software licenses, analytics expertise, long lead times and major upfront capital expenses.

Join this session to learn how you can quickly convert existing storage to Cloud storage, standardize advanced data protection capabilities, and utilize advanced data-driven analytics to optimize tiering across storage systems --- reducing per unit storage costs up to 50% and freeing up valuable IT budget.